The integration of learning and reasoning is a critical challenge in artificial intelligence and machine learning. Neuro-Symbolic AI (NeSy) addresses this by combining symbolic reasoning with neural networks. The authors identify seven dimensions that both Statistical Relational Artificial Intelligence (StarAI) and NeSy share, which can be used to categorize approaches from both fields. These dimensions include logic inference, with model-based and proof-based perspectives, and the construction of a logical architecture, which is then compiled into a computational graph. This framework unifies various NeSy systems, providing a common "recipe" for their development. The process involves generating a "ground" theory represented as an AND/OR tree and transforming it into a continuous and differentiable computational graph, often using algebraic structures.The integration of learning and reasoning is a critical challenge in artificial intelligence and machine learning. Neuro-Symbolic AI (NeSy) addresses this by combining symbolic reasoning with neural networks. The authors identify seven dimensions that both Statistical Relational Artificial Intelligence (StarAI) and NeSy share, which can be used to categorize approaches from both fields. These dimensions include logic inference, with model-based and proof-based perspectives, and the construction of a logical architecture, which is then compiled into a computational graph. This framework unifies various NeSy systems, providing a common "recipe" for their development. The process involves generating a "ground" theory represented as an AND/OR tree and transforming it into a continuous and differentiable computational graph, often using algebraic structures.